Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis of Covid-19
International Journal of Distributed Systems and Technologies
; 14(1), 2023.
Article
Dans Anglais
| Scopus | ID: covidwho-20243534
ABSTRACT
Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.
Concrete Syntax; COVID-19; Graphics Editors; Internet of Things (IoT); Machine Learning; MDE; SIRIUS; Smart Healthcare System; Supervised Learning; Ubiquitous Systems; Internet of things; Modeling languages; Graphic editors; Internet of thing; Machine modelling; Machine-learning; Smart healthcare systems; Ubiquitous application
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
langue:
Anglais
Revue:
International Journal of Distributed Systems and Technologies
Année:
2023
Type de document:
Article
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